Text Classification
Transformers
Safetensors
English
deberta-v2
deberta-v3
human value detection
schwartz values
moral values
political text
retrieval augmented classification
rag
multi-label classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use VictorYeste/value-context-rag-deberta-v3-base-doc-rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VictorYeste/value-context-rag-deberta-v3-base-doc-rag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VictorYeste/value-context-rag-deberta-v3-base-doc-rag")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VictorYeste/value-context-rag-deberta-v3-base-doc-rag") model = AutoModelForSequenceClassification.from_pretrained("VictorYeste/value-context-rag-deberta-v3-base-doc-rag") - Notebooks
- Google Colab
- Kaggle
File size: 432 Bytes
4284792 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | [
"Self-direction: thought",
"Self-direction: action",
"Stimulation",
"Hedonism",
"Achievement",
"Power: dominance",
"Power: resources",
"Face",
"Security: personal",
"Security: societal",
"Tradition",
"Conformity: rules",
"Conformity: interpersonal",
"Humility",
"Benevolence: caring",
"Benevolence: dependability",
"Universalism: concern",
"Universalism: nature",
"Universalism: tolerance"
]
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